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Publications in Math-Net.Ru |
Citations |
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2024 |
| 1. |
A. Yu. Popkov, Yu. A. Dubnov, Yu. S. Popkov, “Entropy-randomized estimation of nonlinear dynamical model parameters on observation of dependent process”, Chelyab. Fiz.-Mat. Zh., 9:1 (2024), 144–159  |
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2023 |
| 2. |
Yu. A. Dubnov, A. Yu. Popkov, B. S. Darkhovsky, “Estimating the Hölder exponents based on the $\epsilon$-complexity of continuous functions: an experimental analysis of the algorithm”, Avtomat. i Telemekh., 2023, no. 4, 19–34 ; Autom. Remote Control, 84:4 (2023), 377–388 |
| 3. |
Yu. A. Dubnov, A. Yu. Popkov, V. Yu. Polishchuk, E. S. Sokol, A. V. Melnikov, Yu. M. Polishchuk, Yu. S. Popkov, “Randomized machine learning algorithms to forecast the evolution of thermokarst lakes area in permafrost zones”, Avtomat. i Telemekh., 2023, no. 1, 98–120 ; Autom. Remote Control, 84:1 (2023), 64–81 |
| 4. |
Yu. A. Dubnov, A. V. Boulytchev, “Approximate estimation using the accelerated maximum entropy method. Part 2. Study of the properties of estimates”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2023, no. 1, 71–81  |
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2022 |
| 5. |
Yu. A. Dubnov, A. V. Boulytchev, “Approximate estimation using the accelerated maximum entropy method. Part 1. Problem statement and implementation for the regression problem”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2022, no. 4, 69–80 |
| 6. |
A. Yu. Popkov, Yu. A. Dubnov, Yu. S. Popkov, “Forecasting of COVID-19 dynamics in EU using randomized machine learning applied to dynamic models”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2022, no. 3, 67–78 |
| 7. |
A. Popkov, Yu. Dubnov, Yu. Popkov, “Randomized machine learning and forecasting of nonlinear dynamic models applied to SIR epidemiological model”, Informatics and Automation, 21:4 (2022), 659–677 |
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2021 |
| 8. |
Yu. A. Dubnov, Yu. A. Dubnov, V. Yu. Polishchuk, Yu. S. Popkov, Yu. M. Polishchuk, A. V. Mel'nikov, E. S. Sokol, “Entropy-randomized method for the reconstruction of missing data”, Avtomat. i Telemekh., 2021, no. 4, 140–160 ; Autom. Remote Control, 82:4 (2021), 670–686   |
| 9. |
Yu. S. Popkov, Yu. A. Dubnov, A. Yu. Popkov, “Entropy-randomized projection”, Avtomat. i Telemekh., 2021, no. 3, 149–168 ; Autom. Remote Control, 82:3 (2021), 490–505 |
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| 10. |
Yu. A. Dubnov, V. Yu. Polishchuk, A. Yu. Popkov, E. S. Sokol, A. V. Mel'nikov, Yu. M. Polishchuk, Yu. S. Popkov, “Entropine-randomized forecasting of the evolution of the area of thermokarst lakes”, Chelyab. Fiz.-Mat. Zh., 6:3 (2021), 384–396 |
| 11. |
Y. Popkov, Y. Dubnov, A. Popkov, “Forecasting development of COVID-19 epidemic in European Union using entropy-randomized approach”, Informatics and Automation, 20:5 (2021), 1010–1033 |
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2020 |
| 12. |
Yu. S. Popkov, A. Yu. Popkov, Yu. A. Dubnov, “Elements of randomized forecasting and its application to daily electrical load prediction in a regional power system”, Avtomat. i Telemekh., 2020, no. 7, 148–172 ; Autom. Remote Control, 81:7 (2020), 1286–1306 |
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| 13. |
Yu. S. Popkov, A. Yu. Popkov, Yu. A. Dubnov, “Deterministic and randomized methods of entropy projection for dimensionality reduction problems”, Inform. Primen., 14:4 (2020), 47–54 |
| 14. |
Yu. A. Dubnov, “Feature selection method based on a probabilistic approach and cross-entropy metric for image recognition problem”, Artificial Intelligence and Decision Making, 2020, no. 2, 78–85 |
| 15. |
Yu. A. Dubnov, A. V. Boulytchev, “On an approach to tuning the Metropolis–Hastings algorithm for the task of separating a mixture of Gaussian components”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2020, no. 1, 25–33 |
| 16. |
Y. S. Popkov, A. Y. Popkov, Y. A. Dubnov, “Cross-entropy reduction of data matrix with restriction on information capacity of projectors and their norms”, Matem. Mod., 32:9 (2020), 35–52 ; Math. Models Comput. Simul., 13:3 (2021), 382–394 |
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2019 |
| 17. |
Yu. A. Dubnov, “Entropy-based evaluation in classification problems”, Avtomat. i Telemekh., 2019, no. 3, 138–151 |
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2018 |
| 18. |
Yu. S. Popkov, Yu. A. Dubnov, A. Yu. Popkov, “Entropy dimension reduction method for randomized machine learning problems”, Avtomat. i Telemekh., 2018, no. 11, 106–122 ; Autom. Remote Control, 79:11 (2018), 2038–2051 |
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| 19. |
Yu. A. Dubnov, “On entropic criteria for feature selection in data analysis problems”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2018, no. 2, 60–69 |
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2017 |
| 20. |
Yu. A. Dubnov, A. V. Boulytchev, “Bayesian identification of a Gaussian mixture model”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2017, no. 1, 101–111 |
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2016 |
| 21. |
Yu. S. Popkov, Yu. A. Dubnov, “Entropy-robust randomized forecasting under small sets of retrospective data”, Avtomat. i Telemekh., 2016, no. 5, 109–127 ; Autom. Remote Control, 77:5 (2016), 839–854 |
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